OBJECTIVES: This study presents a versatile, AI-guided workflow for the targeted isolation and characterization of prenylated flavonoids from Paulownia tomentosa (Thunb.) Steud. (Paulowniaceae). METHODS: The approach integrates established extraction and chromatography-based fractionation protocols with LC-UV-HRMS/MS analysis and supervised machine-learning (ML) custom-trained classification models, which predict prenylated flavonoids from LC-HRMS/MS spectra based on the recently developed Python package AnnoMe (v1.0). RESULTS: The workflow effectively reduced the chemical complexity of plant extracts and enabled efficient prioritization of fractions and compounds for targeted isolation. From the pre-fractionated plant extracts, 2687 features were detected, 42 were identified using reference standards, and 214 were annotated via spectra library matching (public and in-house). Furthermore, ML-trained classifiers predicted 1805 MS/MS spectra as derived from prenylated flavonoids. LC-UV-HRMS/MS data of the most abundant presumed prenyl-flavonoid candidates were manually inspected for coelution and annotated to provide dereplication. Based on this, one putative prenylated (C5) dihydroflavonol (1) and four geranylated (C10) flavanones (2-5) were selected and successfully isolated. Structural elucidation employed UV spectroscopy, HRMS, and 1D as well as 2D NMR spectroscopy. Compounds 1 and 5 were isolated from a natural source for the first time and were named 6-prenyl-4'-O-methyltaxifolin and 3',4'-O-dimethylpaulodiplacone A, respectively. CONCLUSIONS: This study highlights the combination of machine learning with analytical techniques to streamline natural product discovery via MS/MS and AI-guided pre-selection, efficient prioritization, and characterization of prenylated flavonoids, paving the way for a broader application in metabolomics and further exploration of prenylated constituents across diverse plant species.

• Keywords
• bioactive compounds, geranylated flavonoids, prenylated polyphenols, specialized metabolites, untargeted metabolomics,
• Publication type
• Journal Article MeSH

The aim of our study was to determine the PPARγ agonism and hypoglycemic activity of natural phenolics isolated from Paulownia tomentosa and Morus alba. We started with a molecular docking preselection, followed by in vitro cell culture assays, such as PPARγ luciferase reporter gene assay and PPARγ protein expression by Western blot analysis. The ability of the selected compounds to induce GLUT4 translocation in cell culture and lower blood glucose levels in chicken embryos was also determined. Among the thirty-six plant phenolic compounds, moracin M showed the highest hypoglycemic effect in an in ovo experiment (7.33 ± 2.37%), followed by mulberrofuran Y (3.84 ± 1.34%) and diplacone (3.69 ± 1.37%). Neither moracin M nor mulberrofuran Y showed a clear effect on the enhancement of GLUT4 translocation or agonism on PPARγ, while diplacone succeeded in both (3.62 ± 0.16% and 2.4-fold ± 0.2, respectively). Thus, we believe that the compounds moracin M, mulberrofuran Y, and diplacone are suitable for further experiments to elucidate their mechanisms of action.

• Keywords
• PPARγ, diabetes mellitus, hypoglycemic, natural products, plant phenolics,
• MeSH
• Phenols * chemistry   pharmacology   isolation & purification   MeSH
• Hypoglycemic Agents * chemistry   pharmacology   isolation & purification   MeSH
• Chick Embryo MeSH
• Humans MeSH
• Morus * chemistry   MeSH
• Mice MeSH
• PPAR gamma * agonists   metabolism   genetics   chemistry   MeSH
• Glucose Transporter Type 4 metabolism   genetics   MeSH
• Plant Extracts * chemistry   pharmacology   isolation & purification   MeSH
• Molecular Docking Simulation MeSH
• Animals MeSH
• Check Tag
• Chick Embryo MeSH
• Humans MeSH
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Phenols * MeSH
• Hypoglycemic Agents * MeSH
• PPAR gamma * MeSH
• Glucose Transporter Type 4 MeSH
• Plant Extracts * MeSH